PulseExploreJournal ClubDebatesTrendingResearchersJournals
Instagram
HomeExploreJournal ClubTrending
Synapse
⌘+K
Synapse
March 27, 2021International Journal of Environmental Research and Public Health142 citationsOpen Access

Prevalence and Associated Factors of Frailty and Mortality in Patients with End-Stage Renal Disease Undergoing Hemodialysis: A Systematic Review and Meta-Analysis

HLHyeon-Ju LeeYSYoun‐Jung Son

Key Points

Key points are not available for this paper at this time.

Abstract

Hemodialysis is the most common type of treatment for end-stage renal disease (ESRD). Frailty is associated with poor outcomes such as higher mortality. ESRD patients have a higher prevalence of frailty. This systematic review and meta-analysis aimed to identify the prevalence and associated factors of frailty and examine whether it is a predictor of mortality among ESRD patients undergoing hemodialysis. Five electronic databases including PubMed, Embase, CINAHL, Web of Science, and Cochrane Library were searched for relevant studies up to 30 November 2020. A total of 752 articles were found, and seven studies with 2604 participants in total were included in the final analysis. The pooled prevalence of frailty in patients with ESRD undergoing hemodialysis was 46% (95% Confidence interval (CI) 34.2-58.3%). Advanced age, female sex, and the presence of diabetes mellitus increased the risk of frailty in ESRD patients undergoing hemodialysis. Our main finding showed that patients with frailty had a greater risk of all-cause mortality compared with those without (hazard ratio (HR): 2.02, 95% CI: 1.65-2.48). To improve ESRD patient outcomes, healthcare professionals need to assess the frailty of older ESRD patients, particularly by considering gender and comorbidities. Comprehensive frailty screening tools for ESRD patients on hemodialysis need to be developed.

Ask AI
Helpful
Bookmark
Share
View Full Paper

Cite This Study

Lee et al. (2021) studied this question.

synapsesocial.com/papers/69daa63e8988aeabbe6872f1https://doi.org/10.3390/ijerph18073471
Ask AI
Helpful
Bookmark
Share
View Full Paper